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Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

1037
Wang, DongVanderbilt University Medical Center

No involved investigator sites defined.

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

1. Assemble a set of highly-confident peptide identifications as a training set. 2. Generate a ordinal logistic regression model that describes charge segregation. 3. Test the model in peptide identification by modifying predicted fragments.
Biostatistics in R environment.

There are currently no biomarkers annotated for this protocol.


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